from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers.models.xglm.modeling_tf_xglm import (
	    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFXGLMForCausalLM,
	    TFXGLMModel,
	)

@require_tf
class        UpperCamelCase						:
						a__		:str      =			XGLMConfig
						a__		:Union[str, Any]      =			{}
						a__		:Optional[Any]      =			'''gelu'''





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase=14			,     __UpperCamelCase=7			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=99			,     __UpperCamelCase=32			,     __UpperCamelCase=2			,     __UpperCamelCase=4			,     __UpperCamelCase=37			,     __UpperCamelCase="gelu"			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0.1			,     __UpperCamelCase=512			,     __UpperCamelCase=0.02			,     )				->				List[str]:
												UpperCamelCase_		:							List[Any]  =		parent
												UpperCamelCase_		:							Dict  =		batch_size
												UpperCamelCase_		:							int  =		seq_length
												UpperCamelCase_		:							List[Any]  =		is_training
												UpperCamelCase_		:							Tuple  =		use_input_mask
												UpperCamelCase_		:							List[Any]  =		use_labels
												UpperCamelCase_		:							Optional[int]  =		vocab_size
												UpperCamelCase_		:							Optional[int]  =		d_model
												UpperCamelCase_		:							List[str]  =		num_hidden_layers
												UpperCamelCase_		:							int  =		num_attention_heads
												UpperCamelCase_		:							Any  =		ffn_dim
												UpperCamelCase_		:							Optional[int]  =		activation_function
												UpperCamelCase_		:							Optional[int]  =		activation_dropout
												UpperCamelCase_		:							Union[str, Any]  =		attention_dropout
												UpperCamelCase_		:							List[Any]  =		max_position_embeddings
												UpperCamelCase_		:							str  =		initializer_range
												UpperCamelCase_		:							Any  =		None
												UpperCamelCase_		:							Any  =		0
												UpperCamelCase_		:							Optional[int]  =		2
												UpperCamelCase_		:							int  =		1





						def        A_							(self						)				->				int:
												return XGLMConfig.from_pretrained("""facebook/xglm-564M"""						)





						def        A_							(self						)				->				int:
												UpperCamelCase_		:							Union[str, Any]  =		tf.clip_by_value(
												    ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size						)			,     clip_value_min=0			,     clip_value_max=3						)

												UpperCamelCase_		:							Tuple  =		None
												if self.use_input_mask:
																		UpperCamelCase_		:							Optional[Any]  =		random_attention_mask([self.batch_size, self.seq_length]						)

												UpperCamelCase_		:							Optional[Any]  =		self.get_config()

												UpperCamelCase_		:							int  =		floats_tensor([self.num_hidden_layers, self.num_attention_heads]			,     2						)

												return (
												    config,
												    input_ids,
												    input_mask,
												    head_mask,
												)





						def        A_							(self						)				->				Dict:
												return XGLMConfig(
												    vocab_size=self.vocab_size			,     d_model=self.hidden_size			,     num_layers=self.num_hidden_layers			,     attention_heads=self.num_attention_heads			,     ffn_dim=self.ffn_dim			,     activation_function=self.activation_function			,     activation_dropout=self.activation_dropout			,     attention_dropout=self.attention_dropout			,     max_position_embeddings=self.max_position_embeddings			,     initializer_range=self.initializer_range			,     use_cache=_A			,     bos_token_id=self.bos_token_id			,     eos_token_id=self.eos_token_id			,     pad_token_id=self.pad_token_id			,     return_dict=_A			,     )





						def        A_							(self						)				->				int:
												UpperCamelCase_		:							Optional[Any]  =		self.prepare_config_and_inputs()

												(
												    UpperCamelCase_      
												)		:							Optional[int]  =		config_and_inputs

												UpperCamelCase_		:							List[Any]  =		{
												    '''input_ids''': input_ids,
												    '''head_mask''': head_mask,
												}

												return config, inputs_dict


@require_tf
class        UpperCamelCase						(				__a	,	__a	,	unittest.TestCase				):
						a__		:Tuple      =			(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
						a__		:Optional[int]      =			(TFXGLMForCausalLM,) if is_tf_available() else ()
						a__		:Dict      =			(
						    {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
						)
						a__		:int      =			False
						a__		:Dict      =			False
						a__		:int      =			False





						def        A_							(self						)				->				List[Any]:
												UpperCamelCase_		:							Union[str, Any]  =		TFXGLMModelTester(self						)
												UpperCamelCase_		:							str  =		ConfigTester(self			,     config_class=_A			,     n_embd=37						)





						def        A_							(self						)				->				List[Any]:
												self.config_tester.run_common_tests()





						@slow
						def        A_							(self						)				->				Any:
												for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCamelCase_		:							List[Any]  =		TFXGLMModel.from_pretrained(_A						)
																		self.assertIsNotNone(_A						)





						@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""						)
						def        A_							(self						)				->				List[str]:
												super().test_resize_token_embeddings()


@require_tf
class        UpperCamelCase						(				unittest.TestCase				):





						@slow
						def        A_							(self			,     __UpperCamelCase=True						)				->				Any:
												UpperCamelCase_		:							str  =		TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""						)
												UpperCamelCase_		:							Any  =		tf.convert_to_tensor([[2, 268, 9_865]]			,     dtype=tf.intaa						)  # The dog
												# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
												# fmt: off
												UpperCamelCase_		:							Optional[Any]  =		[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
												# fmt: on
												UpperCamelCase_		:							Tuple  =		model.generate(_A			,     do_sample=_A			,     num_beams=1						)
												if verify_outputs:
																		self.assertListEqual(output_ids[0].numpy().tolist()			,     _A						)





						@slow
						def        A_							(self						)				->				Optional[Any]:
												UpperCamelCase_		:							Optional[Any]  =		XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""						)
												UpperCamelCase_		:							Optional[int]  =		TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""						)

												tf.random.set_seed(0						)
												UpperCamelCase_		:							Tuple  =		tokenizer("""Today is a nice day and"""			,     return_tensors="""tf"""						)
												UpperCamelCase_		:							Optional[Any]  =		tokenized.input_ids
												# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
												with tf.device(""":/CPU:0"""						):
																		UpperCamelCase_		:							Optional[int]  =		model.generate(_A			,     do_sample=_A			,     seed=[7, 0]						)
												UpperCamelCase_		:							List[str]  =		tokenizer.decode(output_ids[0]			,     skip_special_tokens=_A						)

												UpperCamelCase_		:							Dict  =		(
												    '''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
												)
												self.assertEqual(_A			,     _A						)





						@slow
						def        A_							(self						)				->				Optional[Any]:
												UpperCamelCase_		:							Union[str, Any]  =		TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""						)
												UpperCamelCase_		:							Optional[int]  =		XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""						)

												UpperCamelCase_		:							str  =		'''left'''

												# use different length sentences to test batching
												UpperCamelCase_		:							List[str]  =		[
												    '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
												    '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
												    '''regardless of whether left padding is applied or not. When''',
												    '''Hello, my dog is a little''',
												]

												UpperCamelCase_		:							Optional[int]  =		tokenizer(_A			,     return_tensors="""tf"""			,     padding=_A						)
												UpperCamelCase_		:							List[Any]  =		inputs['''input_ids''']

												UpperCamelCase_		:							List[str]  =		model.generate(input_ids=_A			,     attention_mask=inputs["""attention_mask"""]			,     max_new_tokens=12						)

												UpperCamelCase_		:							str  =		tokenizer(sentences[0]			,     return_tensors="""tf"""						).input_ids
												UpperCamelCase_		:							Dict  =		model.generate(input_ids=_A			,     max_new_tokens=12						)

												UpperCamelCase_		:							Any  =		tokenizer(sentences[1]			,     return_tensors="""tf"""						).input_ids
												UpperCamelCase_		:							Union[str, Any]  =		model.generate(input_ids=_A			,     max_new_tokens=12						)

												UpperCamelCase_		:							Optional[Any]  =		tokenizer.batch_decode(_A			,     skip_special_tokens=_A						)
												UpperCamelCase_		:							Optional[int]  =		tokenizer.decode(output_non_padded[0]			,     skip_special_tokens=_A						)
												UpperCamelCase_		:							Dict  =		tokenizer.decode(output_padded[0]			,     skip_special_tokens=_A						)

												UpperCamelCase_		:							List[str]  =		[
												    '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
												    '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
												    '''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
												    '''a single''',
												    '''Hello, my dog is a little bit of a shy one, but he is very friendly''',
												]
												self.assertListEqual(_A			,     _A						)
												self.assertListEqual(_A			,     [non_padded_sentence, padded_sentence]						)














'''simple docstring'''





import argparse
import os
import re

import packaging.version


_UpperCamelCase						:						Union[str, Any]  =					'examples/'
_UpperCamelCase						:						List[str]  =					{
    'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(R'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(R'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), R'\1version="VERSION",'),
    'doc': (re.compile(R'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
_UpperCamelCase						:						Optional[Any]  =					{
    'init': 'src/diffusers/__init__.py',
    'setup': 'setup.py',
}
_UpperCamelCase						:						Any  =					'README.md'
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			List[Any]			,       A					:			Tuple		)    ->   Dict:
			with open(A			,       '''r'''			,       encoding='''utf-8'''			,       newline='''\n'''		) as f:
						UpperCAmelCase_      :       int          =       f.read()
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Union[str, Any]          =       REPLACE_PATTERNS[pattern]
			UpperCAmelCase_      :       Tuple          =       replace.replace('''VERSION'''			,       A		)
			UpperCAmelCase_      :       Tuple          =       re_pattern.sub(A			,       A		)
			with open(A			,       '''w'''			,       encoding='''utf-8'''			,       newline='''\n'''		) as f:
						f.write(A		)
def __UpperCAmelCase   (					A					:			List[str]		)    ->   Any:
			for folder, directories, fnames in os.walk(A		):
						# Removing some of the folders with non-actively maintained examples from the walk
						if "research_projects" in directories:
									directories.remove('''research_projects'''		)
						if "legacy" in directories:
									directories.remove('''legacy'''		)
						for fname in fnames:
									if fname.endswith('''.py'''		):
												update_version_in_file(os.path.join(A			,       A		)			,       A			,       pattern='''examples'''		)
def __UpperCAmelCase   (					A					:			Tuple			,       A					:			Optional[int]=False		)    ->   Tuple:
			for pattern, fname in REPLACE_FILES.items():
						update_version_in_file(A			,       A			,       A		)
			if not patch:
						update_version_in_examples(A		)
def __UpperCAmelCase   (					)    ->   int:
			UpperCAmelCase_      :       List[str]          =       '''🤗 Transformers currently provides the following architectures'''
			UpperCAmelCase_      :       int          =       '''1. Want to contribute a new model?'''
			with open(A			,       '''r'''			,       encoding='''utf-8'''			,       newline='''\n'''		) as f:
						UpperCAmelCase_      :       Optional[int]          =       f.readlines()

			# Find the start of the list.
			UpperCAmelCase_      :       List[str]          =       0
			while not lines[start_index].startswith(_start_prompt		):
						start_index += 1
			start_index += 1

			UpperCAmelCase_      :       Any          =       start_index
			# Update the lines in the model list.
			while not lines[index].startswith(_end_prompt		):
						if lines[index].startswith('''1.'''		):
									UpperCAmelCase_      :       Optional[Any]          =       lines[index].replace(
									    '''https://huggingface.co/docs/diffusers/main/model_doc'''			,       '''https://huggingface.co/docs/diffusers/model_doc'''			,       )
						index += 1

			with open(A			,       '''w'''			,       encoding='''utf-8'''			,       newline='''\n'''		) as f:
						f.writelines(A		)
def __UpperCAmelCase   (					)    ->   Dict:
			with open(REPLACE_FILES['''init''']			,       '''r'''		) as f:
						UpperCAmelCase_      :       Any          =       f.read()
			UpperCAmelCase_      :       Union[str, Any]          =       REPLACE_PATTERNS['''init'''][0].search(A		).groups()[0]
			return packaging.version.parse(A		)
def __UpperCAmelCase   (					A					:			str=False		)    ->   List[Any]:
			UpperCAmelCase_      :       Union[str, Any]          =       get_version()
			if patch and default_version.is_devrelease:
						raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''		)
			if default_version.is_devrelease:
						UpperCAmelCase_      :       Optional[int]          =       default_version.base_version
			elif patch:
						UpperCAmelCase_      :       Any          =       F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
			else:
						UpperCAmelCase_      :       Optional[Any]          =       F"{default_version.major}.{default_version.minor + 1}.0"

			# Now let's ask nicely if that's the right one.
			UpperCAmelCase_      :       List[str]          =       input(F"Which version are you releasing? [{default_version}]"		)
			if len(A		) == 0:
						UpperCAmelCase_      :       Tuple          =       default_version

			print(F"Updating version to {version}."		)
			global_version_update(A			,       patch=A		)
def __UpperCAmelCase   (					)    ->   Optional[int]:
			UpperCAmelCase_      :       str          =       get_version()
			UpperCAmelCase_      :       Tuple          =       F"{current_version.major}.{current_version.minor + 1}.0.dev0"
			UpperCAmelCase_      :       Any          =       current_version.base_version

			# Check with the user we got that right.
			UpperCAmelCase_      :       List[Any]          =       input(F"Which version are we developing now? [{dev_version}]"		)
			if len(A		) == 0:
						UpperCAmelCase_      :       Optional[int]          =       dev_version

			print(F"Updating version to {version}."		)
			global_version_update(A		)


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
						_UpperCamelCase						:						Tuple  =					argparse.ArgumentParser()
						parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
						parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
						_UpperCamelCase						:						Optional[Any]  =					parser.parse_args()
						if not args.post_release:
												pre_release_work(patch=args.patch)
						elif args.patch:
												print('Nothing to do after a patch :-)')
						else:
												post_release_work()


